A genetic algorithm for scheduling flexible manufacturing systems

1998 ◽  
Vol 14 (8) ◽  
pp. 588-607 ◽  
Author(s):  
N. Jawahar ◽  
P. Aravindan ◽  
S. G. Ponnambalam
2011 ◽  
Vol 467-469 ◽  
pp. 1680-1685
Author(s):  
Zhi Feng Liu ◽  
Jian Tan ◽  
Guo Ping An ◽  
Jian Hua Wang

Models for the layout of production lines have been studied in this paper. First, it constructed a model of Multi-row Mix Integer Programming for the Flexible Manufacturing Systems. Secondly, using the genetic algorithms to analyze, it established the effective solutions. Finally, it completed the evaluation of the program. Demonstrating the feasibility and effectiveness method through the case of studies, a new method was given for the layout of large-scale production line.


Author(s):  
Vijay Kumar M. ◽  
A. N. N. Murthy ◽  
K. C. Chandrashekara

Flexible manufacturing systems (FMS) have already proved their great success in a large number of manufacturing industries. Realizing the importance of FMS in increasing productivity, quality, the high investment and the potential of FMS as a strategic competitive tool makes it attractive to engage in research in this area. Scheduling of flexible manufacturing systems (FMSs) has been one of the most attractive areas for both researches and practitioners. A considerable body of literature has accumulated in this area since the late 1970s when the first batches of papers were published. A number of approaches here been adopted to schedule FMS. The FMS scheduling problem has been tackled by various traditional optimization techniques and non-traditional approaches. The traditional method can give an optimal solution to small-scale problem; they are often inefficient when applied to larger-scale problem. The non-traditional approaches such as genetic algorithm generate optimal schedule to large-scale problems. Articles emphasizing many methodological perspectives are critically reviewed. The review is done from multiple viewpoints covering different approaches like simulation, artificial intelligence and genetic algorithm. Comments on the publications and suggestions for research and development are given. A comprehensive bibliography is also presented in the paper.


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